Drift scores are numerical values that measure how much data has changed over time compared to a previous dataset. They help identify shifts or changes in the patterns, distributions, or characteristics of data. These scores are often used to monitor whether data used by a machine learning model is still similar to the data it…
Performance metrics are measurements used to assess how well a system, process, or individual is working. They help track progress, identify strengths and weaknesses, and guide improvements. Good metrics are clear, relevant, and easy to understand so that everyone involved can use them to make better decisions.
Model accuracy measures how often a predictive model makes correct predictions compared to the actual outcomes. It is usually expressed as a percentage, showing the proportion of correct predictions out of the total number of cases. High accuracy means the model is making reliable predictions, while low accuracy suggests it may need improvement.
Drift detection is a process used to identify when data or patterns change over time, especially in automated systems like machine learning models. It helps ensure that models continue to perform well, even if the underlying data shifts. Detecting drift early allows teams to update, retrain, or adjust their systems to maintain accuracy and reliability.
Prompt regression refers to a gradual decline in the effectiveness or accuracy of responses generated by an AI language model when using a specific prompt. This can happen when updates to the model or system unintentionally cause it to interpret prompts differently or produce less useful answers. Prompt regression is a concern for developers who…
Weighted sampling is a method for selecting items from a group where some items are given a higher chance of being chosen than others. Each item is assigned a weight, which indicates its importance or likelihood of selection. This approach is often used when some options are more relevant or common than others, so the…
Model chaining is a technique in artificial intelligence where the output from one model is used as the input for another model. This allows multiple models to work together to solve complex tasks that a single model might not handle well alone. By passing information through a sequence of models, each step can add value…
Model hardening refers to techniques and processes used to make machine learning models more secure and robust against attacks or misuse. This can involve training models to resist adversarial examples, protecting them from data poisoning, and ensuring they do not leak sensitive information. The goal is to make models reliable and trustworthy even in challenging…
Sequence folding is a process that takes a long list or sequence of items and combines them into a single result by applying a specific operation step by step. The operation is usually something simple, like adding numbers together or joining words, but it can be more complex depending on the task. This method is…
Logic sampling is a method used to estimate probabilities in complex systems, like Bayesian networks, by generating random samples that follow the rules of the system. Instead of calculating every possible outcome, it creates simulated scenarios and observes how often certain events occur. This approach is useful when direct calculation is too difficult or time-consuming.